In cross-functional data analytics projects, which team should be engaged from the outset to maximize success?

• A. Underwriting
• B. Data science
• C. Actuary
• D. IT

Answer: (B)

Engaging the data science team from the outset ensures the analytics effort is grounded in a clear, testable problem and a practical plan to solve it. Data scientists translate business questions into hypotheses, design an appropriate modeling approach, and map out the data needs, sources, and data quality requirements. They define the evaluation criteria and success metrics early, which helps prevent scope creep and rework. By leading the initial framing, they also set up a reproducible workflow and realistic timelines, and they can coordinate with IT for data access and governance while aligning with domain experts to ensure relevance.

Starting with domain specialists like underwriting or actuarial is valuable for domain insight, but without a data‑science-led framing, the project may chase ambiguous questions or misaligned metrics. IT is crucial for data access and infrastructure, yet its strength lies in building and maintaining systems, not primarily in shaping the analytic approach. For these reasons, bringing in the data science team first best positions cross-functional analytics projects for speed, clarity, and measurable outcomes.